Recent works [1]}, [2]} have highlighted the superiority of L2 normalization, we therefore adopt the cosine similarity \(S\)  as the default metric. With L2 normalization, the squared Euclidean distance \(D\)  equals to \(2-2S\)  so that \(S\)  and \(D\)  are equivalent as metrics. Although there are still a number of methods [3]}, [4]} utilizing the Euclidean distance without L2 normalization, [3]} shows that the cosine similarity performs well as the evaluation metric. We further empirically show that the cosine similarity works well for explanation in this case (Section REF ).
